It has long been a goal to program machines to process human-readable language, sometimes in part as an effort to generate artificial intelligence. However, programming computers to process human-readable language has proven to be far more difficult than imagined, particularly as languages continue to change and evolve, and the meaning of words and phrases are more ambiguous and nuanced than assumed. A number of techniques are available for processing natural language by computers, but the methods for generating these models either are inaccurate and imprecise or require months of refinement and programming to accurately model specific subject areas of language. It is desirable, therefore, to develop improved methods for generating natural language models that are accurate and quick while also reducing human time spent generating the models.